AI-Driven Decision Support for Sickle Cell Disease Detection in Smart Cities: An Innovative Approach Using Hospital Data from Saudi Arabia
PI: Dr. Awsan Mohammed
CoI: Dr. Maged Saleh, Dr. Ahmad Ghaithan, Dr. Ibrahim Al Turki
CoI: Dr. Maged Saleh, Dr. Ahmad Ghaithan, Dr. Ibrahim Al Turki
- Sickle Cell Disease is prevalent (2-4% in some regions) and costly, driving frequent hospitalizations, morbidity, and strain on healthcare resources, necessitating innovative solutions.
- Utilizing interconnected EHR systems, real-time analytics, and IoT health monitoring within Saudi smart cities can enhance early SCD detection and proactive care straegies.
- A deep learning model trained on anonymized, multi-hospital EHR data aims to improve SCD diagnosis accuracy and predict complications, enabling timely, data-driven clinical decisions.
- The project seeks to reduce unnecessary admissions, optimize healthcare resource allocation, and lower costs through predictive analytics integrated into smart city infrastructure.
- Successful implementation could serve as a blueprint for regions adopting smart city frameworks, promoting adaptable, scalable solutions to alleviate SCD’s global health burden.
